Factors associated with catastrophic health expenditure in sub-Saharan Africa: A systematic review

Objective A non-negligible proportion of sub-Saharan African (SSA) households experience catastrophic costs accessing healthcare. This study aimed to systematically review the existing evidence to identify factors associated with catastrophic health expenditure (CHE) incidence in the region. Methods We searched PubMed, CINAHL, Scopus, CNKI, Africa Journal Online, SciELO, PsycINFO, and Web of Science, and supplemented these with search of grey literature, pre-publication server deposits, Google Scholar®, and citation tracking of included studies. We assessed methodological quality of included studies using the Appraisal tool for Cross-Sectional Studies for quantitative studies and the Critical Appraisal Skills Programme checklist for qualitative studies; and synthesized study findings according to the guidelines of the Economic and Social Research Council. Results We identified 82 quantitative, 3 qualitative, and 4 mixed-methods studies involving 3,112,322 individuals in 650,297 households in 29 SSA countries. Overall, we identified 29 population-level and 38 disease-specific factors associated with CHE incidence in the region. Significant population-level CHE-associated factors were rural residence, poor socioeconomic status, absent health insurance, large household size, unemployed household head, advanced age (elderly), hospitalization, chronic illness, utilization of specialist healthcare, and utilization of private healthcare providers. Significant distinct disease-specific factors were disability in a household member for NCDs; severe malaria, blood transfusion, neonatal intensive care, and distant facilities for maternal and child health services; emergency surgery for surgery/trauma patients; and low CD4-count, HIV and TB co-infection, and extra-pulmonary TB for HIV/TB patients. Conclusions Multiple household and health system level factors need to be addressed to improve financial risk protection and healthcare access and utilization in SSA. Protocol registration PROSPERO CRD42021274830

pooled effect estimates. Random effects meta-analysis allows for differences in the treatment effect from study to study because of real differences in the treatment effect in each study as well as sampling variability [14]. Analyses were conducted using Stata version 16.1 (STATA Corp, College Station, TX). Where meta-analysis was not possible due to difference in the definition of CHEassociated factors, we analyzed the reported quantitative estimates narratively.
For qualitative data, we independently performed line-by-line coding of text to group similar concepts and developed new codes when necessary. We organized free codes into descriptive major themes and sub-themes using an inductive approach as detailed by Thomas and Harden [15]. Each reviewer first did this independently and then as a group. Through discussion more abstract or analytical themes emerged and we resolved discrepancies between reviewers through discussion and consensus was achieved on all occasions. Finally, we globally assessed findings from both quantitative studies including meta-analysis for each CHE-associated factor-based of breadth of evaluation in included studies, consistency of an effect on CHE incidence, and methodological quality of included studies evaluating this factor-and when available, triangulated these with the participants' lived experiences reported in qualitative studies to categorize each CHE-associated factor as either significant or marginal. We categorized a factor as "significant" if it was widely evaluated factors that consistently diminished or exaggerated the likelihood of CHE incidence. Otherwise, we categorized such factor as "marginal".

Deviations from study protocol
The original protocol was for a quantitative study. We decided to include qualitative studies to enrich our understanding of the key drivers of CHE based on individuals' lived experiences, which population-based quantitative studies do not cover.

Study characteristics
We identified 965 unique articles published between 2000 and 2021 (Fig 1). Of these articles, 122 full-text articles were screened for eligibility and 89 studies met inclusion criteria for this review   (Table 1). Included studies were 80 peer-reviewed publications, four working papers, and five dissertations, and covered 3,112,322 individuals in 650,297 households in 29 SSA countries. Included articles were published between 2005 to 2021 (Fig 2); were predominantly English-language articles (n = 85; 95.5%); mostly used nationally-representative samples (n = 48; 53.9%); and mostly estimated CHE incidence using 'non-food expenditure' definition (n = 53; 59.6%)- Table 2.
Of the 89 included studies, 70 (78.6%) were rated as high quality, 16 (18.0%) as moderate quality, and the remaining 3 (3.6%) as low quality- Table 1. Of note, all included quantitative studies used sample frames that closely represented the target population (AXIS tool Item 5) and used selection procedures that likely selected samples representative of the underlying population (AXIS tool Item 6). Also, included qualitative studies used sampling techniques that ensured the identification and selection of individuals that recently suffered catastrophic health expenses.

Catastrophic health expenditure-associated factors
Included studies involved 82 population-based studies reporting quantitative estimates, of which a total of 73 were included in the 71 different random-effects meta-analysis. Nine studies were included in narrative synthesis. Quantitative data from four mixed methods studies were also included in the narrative synthesis. Results from quantitative meta-analysis were reported in two broad categories: population-level factors and disease-specific factors ( Tables  3 and 4). Seven studies reporting qualitative data (3 qualitative studies and 4 mixed-methods) met the inclusion criteria, all of which were included in thematic analysis ( Table 5). Qualitative data revealed two main themes associated with households' CHE incidence: low socioeconomic status and being uninsured ( Table 6). We presented excerpts of supportive qualitative findings with the relevant quantitative findings and a thematic analysis map in S1 Fig.

Study language
• English 85 (95.5%) • The poorest households were at a higher risk of CHE than richer households [28,43,46,51,53,81,87,91], as the following statement from a respondent reflects: "I got treatment for my first child from the hospital, and they charged us a lot of money. We did not have anything left after, and my husband was hiding. After a long time, we were able to borrow money from a relative. . ." [87] Health insurance coverage and social safety nets both protect households from CHE, although quantitative analysis suggests this protection is inconsistent.    [51].

Content analysis 41 IDIs and 7 FGDs
To explore intra-household resource allocation, focusing on how families prioritize newborn health and household needs in Ethiopia; and to explore coping strategies families use to manage these priorities.
� Even though child and maternal health services are supposed to be provided free of charge at the health center level, families still suffer CHE incidence for newborn care at the hospital. � Families are forced to choose between potential worsening of the baby's health on the one hand, and risking unbearable newborn healthcare costs or financial consequences for the family when taking the newborn to hospital � The poorest households are most faced by CHE incidence from newborn care, with little or no coping mechanism a Only findings from qualitative analysis were reported here. Quantitative data in mixed methods studies were included meta-analysis and narrative synthesis. https://doi.org/10.1371/journal.pone.0276266.t005 Health system factors. Several studies evaluated the link between CHE incidence and the level of health facility were care was sought [25,35,45,56,67], health facility type [17,21,25,35,39,45,52,56,68,73,75,93,94], distance to health facility [41,46,56,58,62,65,67,68,93], number of health facilities in district/county [28], and prior care from traditional healers [27,34]. Of these, health facility type, and health facility level were significantly associated with CHE incidence- Table 3. A few studies, however, showed that accessing care from private healthcare providers decreased households' risk of catastrophic expenditure, although the level and type of care sought from these providers was not clear [21,52,93].  [77] ". . .Yes, I was delayed because of money problem so I was a bit delayed" [81] "If I go to the hospital with my child, there is no one who can properly give food for the others, there is no one to wash them or send them to school properly. They will not go to school and also there will be no one to buy them books." [87] Poor households that access care face financial catastrophe 11 5 "We sold our land (USD 805) to access treatment" [79]. "Another said, "We sold food stuff (USD 54), 2 goats (USD 97), a bull (USD 258), a pig (USD 43)" [79] "I had asked the nurses to keep my baby if they wanted, and to let me go look for money until I could pull together the necessary sum." [77] Lost income-earning opportunities complicates access to care "Only a few people benefit from insurance schemes-like civil servants, or people whose employers have an insurance scheme-and have access to this programme (for pre-therapeutic assessment), but they still have to pay for the injections." [36] "Quite a number of people here are not extremely poor because healthcare in Nigeria is not cheap. The extreme poor will not come to the hospital because insurance is minimal, although some may come when it is life-threatening." [51].
Health insurance enrolment should be encouraged Other factors. Other marginal factors linked with CHE incidence at the population level include violence against women [34], house ownership [46], business ownership [35], and regular use of mosquito bed nets [17,52]- Table 3.
Having health insurance was protective of catastrophic costs [51,71,80]-as in the population level.
"I had asked the nurses to keep my baby if they wanted, and to let me go look for money until I could pull together the necessary sum." [77] "I got treatment for my first child from the hospital, and they charged us a lot of money. We did not have anything left after, and my husband was hiding. After a long time, we were able to borrow money from a relative. . ." [87] Surgery and trauma care. For households that sought surgical or trauma care, CHE incidence was associated with residence, socioeconomic status, health insurance status, and sex, age, marital status, education, and employment status of household head- Table 4. Other factors include old age, hospitalization, healthcare provider type, specialist care, intensive care unit admission, and emergency surgery [31,40,79,81,82,84,89,90].
Malaria. The included studies identified six sociodemographic factors-household residence, socioeconomic status, household head's sex, age, education, and employment statusand two health system factors: healthcare provider type and distance to the health facility [54,97]. Of these, only socioeconomic status was significantly associated with CHE incidence for malaria treatment ( Table 4).
Neglected tropical diseases (NTDs). For households that sought healthcare for NTDs, seven socio-demographic factors-household residence, socio-economic status, health insurance, and the sex, age, education, and religion of the patients-were linked with CHE incidence [37,85] (Table 4). Of these factors, only socioeconomic status was significantly associated with CHE incidence.

Discussion
Factors associated with CHE incidence among SSA households are multidimensional and diverse. Overall, a few points emerge from this review. First, the majority of included studies used regression analysis to evaluate the factors associated with CHE incidence. Given that included studies utilized different definitions for evaluated factors, meta-analysis was possible for fewer included studies. However, all included studies were evaluated and synthesized narratively. Secondly, studies evaluating CHE incidence in SSA countries mostly used the 'capacity-to-pay' or 'non-food expenditure' definition while fewer studies used the ratio of OOP to total household income [7]. However, studies that used both definitions suggests that CHEassociated factors were largely similar between the definitions [19,21,30,60,68,78,92,93]. Reporting CHE incidence and CHE-associated factors using both definitions enhances comparability between studies. Also, despite the progress SSA countries have made towards universal health insurance, households are still exposed to CHE [46,66,84]. Yet, it is likely that many low-income uninured households in SSA countries without universal insurance choose not to seek health care rather than face the financial hardship associated with out-of-pocket healthcare payments [46,51,99].
At the population level, our review highlights rural residence, low socioeconomic status, lack of health insurance, advanced age, chronic illness, hospitalization, utilization of private healthcare provider, and utilization of specialist care as the most significant determinants of CHE incidence. Our findings are consistent with findings in comparable regions such as Southeast Asia [105,106] and South America [107,108]. Due to widespread poverty, most SSA households cannot afford insurance premiums and so rely on OOP payment for healthcare [2,109]. Given the highly regressive impact of OOP payment [2,3], most studies in SSA region demonstrate households' socioeconomic status as a risk factor for CHE [3,109]. Rural residence in SSA countries is a proximal indicator of limited household income [50,91,103]. This is compounded by lack of health facilities in the rural settings, transportation costs to reach urban health facilities, or the indirect expenditure, such as the costs incurred by an accompanying caretaker [20,21,76,91]. Having an elderly person in the household increases the chances of incurring CHE [21,26,63,103]. This is as expected because elderly persons require more healthcare [21], and are more likely to have chronic illnesses [26,28]. Both factors increase health expenditures and often require working family members to quit their jobs. Hospitalization, utilization of private healthcare provider, and/or specialist (tertiary) healthcare all increase the possibility of incurring CHE [25,41,62,75,94]. Given that most SSA countries do not have financial risk protection mechanisms in place, this situation is even grim as the CHE definitions used in included studies does not consider households with unmet healthcare needs.
Factors distinctly associated with CHE incidence at the disease-specific level include disability in a household member for NCDs; severe malaria, blood transfusion, and distant health facilities for maternal and child health services; emergency/unplanned surgery for surgery and trauma patients; and low CD4 count, HIV and TB co-infection, and extra-pulmonary TB for HIV and TB patients. For households affected by NCDs, disability imposes further financial burden in the form of extra health expenses and lost income [51]. The farther the distance of health facilities from the place of residence, the higher the direct non-medical costs, including transportation and accommodation costs. Hence, rural households are therefore more likely to incur CHE for maternal and child healthcare [22,97]. For similar reasons, blood transfusion and severe malaria treatments are rarely available at rural health facilities, and require hospitalization and specialist care-which increase CHE risks [22,54]. For patients requiring HIV and TB care, low CD4-count, HIV and TB co-infection, and extra-pulmonary TB are all indicative of poor health status requiring increased usage of healthcare services with a higher risk of incurring CHE [24,29,102].

Strengths and limitations
To the best of our knowledge, this is the first systematic review to comprehensively map the factors associated with CHE incidence in SSA. We also identified determinants for both population and disease-specific level CHE incidence which enables easy identification of populations that are most at risk for community-wide and/or vertical disease-specific interventions. Furthermore, our review combined both quantitative and qualitative studies to synthesize evidence that is both generalizable and sufficiently nuanced.
Our study has a few limitations. First, our review does not capture factors associated with households who cannot meet treatment costs-a gap that future studies can address using new variables that capture these households. Also, as we identified determinants of CHE incidence using two thresholds, we may have missed some factors that might have been reported using other thresholds. Thirdly, there is the inherent difficulty in mapping and adjudicating the evidence on these factors identified from the studies as either significant or marginal. Ultimately, these were subjective judgments based on the authors' understanding of the texts in included studies that are not as error-proof as might be hoped for. To address this, a multi-rater system was used-each factor was independently adjudicated by at least two authors-to minimize subjectivity. Finally, our categorization of some determinants as marginal does not imply dismissal of the influence of these factors in some unique settings. In some settings and for different households, these "marginal" factors could have greater eminence.

Policy implications
Our review provides significant contextual evidence for policy discussion and health financing reforms by identifying the sociodemographic characteristics of households that are most likely to suffer financial catastrophe in SSA countries. This is a critical step toward developing comprehensive social protection mechanisms-a key vehicle for achieving UHC. Our study provides key details for fine-tuning the different means of identifying households for targeted or supplemental protection such as means testing, proximal means testing, geographic targeting, or participatory wealth ranking [109].

Conclusion
Our study suggests that the key factors associated with population and disease-specific CHE incidence in SSA countries are rural residence, low socioeconomic status, lack of health insurance, having an elderly household member, chronic illness, hospitalization, use of private healthcare providers, and use of tertiary/specialist healthcare. Highlighting these factors in a comprehensive review underscores potential strategies for implementing/improving financial risk protection measures to achieve UHC in these SSA countries.
Supporting information S1